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A New Representation of Skeleton Sequences for 3D Action Recognition

https://doi.org/10.1109/cvpr.2017.486

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4615-5529-2_5openalex-snapshot

Multitask Learning

inferredcites →https://doi.org/10.1007/978-3-319-48881-3_28openalex-snapshot

Human Interaction Prediction Using Deep Temporal Features

inferredcites →https://doi.org/10.1007/978-3-642-24797-2_3openalex-snapshot

Neural Networks

inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot

ImageNet Large Scale Visual Recognition Challenge

inferredcites →https://doi.org/10.1016/j.cviu.2006.07.013openalex-snapshot

Free viewpoint action recognition using motion history volumes

inferredcites →https://doi.org/10.1016/j.cviu.2017.01.011openalex-snapshot

Space-time representation of people based on 3D skeletal data: A review

inferredcites →https://doi.org/10.1021/ac60214a047openalex-snapshot

Smoothing and Differentiation of Data by Simplified Least Squares Procedures.

inferredcites →https://doi.org/10.1109/cvpr.2012.6247813openalex-snapshot

Mining actionlet ensemble for action recognition with depth cameras

inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot

Multi-column deep neural networks for image classification

inferredcites →https://doi.org/10.1109/cvpr.2014.528openalex-snapshot

Is Rotation a Nuisance in Shape Recognition?

inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2014.82openalex-snapshot

Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group

inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot

Going deeper with convolutions

inferredcites →https://doi.org/10.1109/cvpr.2015.7299172openalex-snapshot

Jointly learning heterogeneous features for RGB-D activity recognition

inferredcites →https://doi.org/10.1109/cvpr.2015.7299176openalex-snapshot

Modeling video evolution for action recognition

inferredcites →https://doi.org/10.1109/cvpr.2016.115openalex-snapshot

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis

inferredcites →https://doi.org/10.1109/cvprw.2012.6239233openalex-snapshot

View invariant human action recognition using histograms of 3D joints

inferredcites →https://doi.org/10.1109/cvprw.2012.6239234openalex-snapshot

Two-person interaction detection using body-pose features and multiple instance learning

inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

inferredcites →https://doi.org/10.1109/icassp.2013.6638947openalex-snapshot

Speech recognition with deep recurrent neural networks

inferredcites →https://doi.org/10.1109/icassp.2015.7178838openalex-snapshot

Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks

inferredcites →https://doi.org/10.1109/iccv.2015.460openalex-snapshot

Differential Recurrent Neural Networks for Action Recognition

inferredcites →https://doi.org/10.1109/iccv.2015.505openalex-snapshot

Category-Blind Human Action Recognition: A Practical Recognition System

inferredcites →https://doi.org/10.1109/icmew.2014.6890714openalex-snapshot

Interactive body part contrast mining for human interaction recognition

inferredcites →https://doi.org/10.1109/icpr.2014.772openalex-snapshot

Skeletal Quads: Human Action Recognition Using Joint Quadruples

inferredcites →https://doi.org/10.1109/lsp.2017.2690339openalex-snapshot

SkeletonNet: Mining Deep Part Features for 3-D Action Recognition

inferredcites →https://doi.org/10.1109/tip.2013.2295753openalex-snapshot

Latent Hierarchical Model of Temporal Structure for Complex Activity Classification

inferredcites →https://doi.org/10.1109/tpami.2013.65openalex-snapshot

Temporal Localization of Actions with Actoms

inferredcites →https://doi.org/10.1145/2733373.2807412openalex-snapshot

MatConvNet

inferredcites →https://doi.org/10.1145/2964284.2967191openalex-snapshot

Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Hybrid features for skeleton‐based action recognition based on network fusion

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CHAN : Skeleton based action recognition by multi‐level feature learning

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Deep Bilinear Learning for RGB-D Action Recognition

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Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition

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Adding Attentiveness to the Neurons in Recurrent Neural Networks

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Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning

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An Attention-Aware Model for Human Action Recognition on Tree-Based Skeleton Sequences

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Global Regularizer and Temporal-Aware Cross-Entropy for Skeleton-Based Early Action Recognition

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High-Order Graph Convolutional Network for Skeleton-Based Human Action Recognition

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Temporal Invariant Factor Disentangled Model for Representation Learning

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DeepGRU: Deep Gesture Recognition Utility

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Deep Residual Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

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Leveraging Pre-trained CNN Models for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-36711-4_2openalex-snapshot

Multi-person 3D Pose Estimation from Monocular Image Sequences

inferred← citeshttps://doi.org/10.1007/978-3-030-58565-5_45openalex-snapshot

DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-58571-6_3openalex-snapshot

Adversarial Self-supervised Learning for Semi-supervised 3D Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-58577-8_2openalex-snapshot

Structure-Aware Human-Action Generation

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Multi-view Action Recognition Using Cross-View Video Prediction

inferred← citeshttps://doi.org/10.1007/978-3-030-58586-0_32openalex-snapshot

Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-58621-8_25openalex-snapshot

HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction

inferred← citeshttps://doi.org/10.1007/978-3-030-61864-3_30openalex-snapshot

3D Geology Scene Exploring Base on Hand-Track Somatic Interaction

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A Discriminative STGCN for Skeleton Oriented Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-68590-4_2openalex-snapshot

Skeleton-Based Activity Recognition: Preprocessing and Approaches

inferred← citeshttps://doi.org/10.1007/978-3-030-68790-8_43openalex-snapshot

Recurrent Graph Convolutional Network for Skeleton-Based Abnormal Driving Behavior Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-68821-9_33openalex-snapshot

A Novel Multi-feature Skeleton Representation for 3D Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-69541-5_21openalex-snapshot

Spatial Temporal Attention Graph Convolutional Networks with Mechanics-Stream for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-75490-7_5openalex-snapshot

Skeleton-Based Human Action Recognition on Large-Scale Datasets

inferred← citeshttps://doi.org/10.1007/978-3-030-86365-4_20openalex-snapshot

Local and Non-local Context Graph Convolutional Networks for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-86365-4_24openalex-snapshot

Structure-Aware Multi-scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-89370-5_12openalex-snapshot

Graph Attention Convolutional Network with Motion Tempo Enhancement for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-030-92270-2_53openalex-snapshot

Spatial-Temporal Attention Network with Multi-similarity Loss for Fine-Grained Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-02375-0_13openalex-snapshot

Distance-GCN for Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-02375-0_7openalex-snapshot

Spatio-temporal Weight of Active Region for Human Activity Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-12426-6_9openalex-snapshot

Skeleton-Based Mutual Action Recognition Using Interactive Skeleton Graph and Joint Attention

inferred← citeshttps://doi.org/10.1007/978-3-031-13844-7_3openalex-snapshot

Skeleton-Based Hand Gesture Recognition by Using Multi-input Fusion Lightweight Network

inferred← citeshttps://doi.org/10.1007/978-3-031-14771-5_13openalex-snapshot

A Step Towards Automated Functional Assessment of Activities of Daily Living

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Graph Convolutional Networks (GCN)-Based Lightweight Detection Model for Dangerous Driving Behavior

inferred← citeshttps://doi.org/10.1007/978-3-031-19772-7_3openalex-snapshot

Contrastive Positive Mining for Unsupervised 3D Action Representation Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-19772-7_9openalex-snapshot

Collaborating Domain-Shared and Target-Specific Feature Clustering for Cross-domain 3D Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-19806-9_35openalex-snapshot

IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-19809-0_11openalex-snapshot

Hierarchically Self-supervised Transformer for Human Skeleton Representation Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-19821-2_33openalex-snapshot

MVSTER: Epipolar Transformer for Efficient Multi-view Stereo

inferred← citeshttps://doi.org/10.1007/978-3-031-19830-4_38openalex-snapshot

ERA: Expert Retrieval and Assembly for Early Action Prediction

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CMD: Self-supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation

inferred← citeshttps://doi.org/10.1007/978-3-031-20497-5_26openalex-snapshot

Part-Wise Topology Graph Convolutional Network for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-20713-6_26openalex-snapshot

Depth-Based vs. Color-Based Pose Estimation in Human Action Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-20868-3_35openalex-snapshot

JointContrast: Skeleton-Based Mutual Action Recognition with Contrastive Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-25072-9_39openalex-snapshot

Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns

inferred← citeshttps://doi.org/10.1007/978-3-031-26316-3_10openalex-snapshot

Focal and Global Spatial-Temporal Transformer for Skeleton-Based Action Recognition

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Spatial-Temporal Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

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3D-Yoga: A 3D Yoga Dataset for Visual-Based Hierarchical Sports Action Analysis

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition

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Human Actions Recognition System Based on Neural Networks

inferred← citeshttps://doi.org/10.1007/978-3-031-46311-2_19openalex-snapshot

Multi-modal Stream Fusion for Skeleton-Based Action Recognition

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MS-GTR: Multi-stream Graph Transformer for Skeleton-Based Action Recognition

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Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition

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Advancing Multi-actor Graph Convolutions for Skeleton-Based Action Recognition

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Deep Learning Techniques for Skeleton-Based Action Recognition: A Survey

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Learning by Aligning 2D Skeleton Sequences and Multi-modality Fusion

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Towards Physical World Backdoor Attacks Against Skeleton Action Recognition

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LORTSAR: Low-Rank Transformer for Skeleton-Based Action Recognition

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Mask and Compress: Efficient Skeleton-Based Action Recognition in Continual Learning

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HAPtics: Human Action Prediction in Real-time via Pose Kinematics

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Joint-Temporal Action Segmentation via Multi-action Recognition

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HybridFormer: Bridging Local and Global Spatio-Temporal Dynamics for Efficient Skeleton-Based Action Recognition

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A New Feature Extraction Algorithm for Skeleton-Based Human Action Recognition

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Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN

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Two-Stream Transformer Encoders for Skeleton-Based Action Recognition

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ieSTGCN:A Mining Model of Skeleton Spatio-temporal Graph

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Semantic Adaptive Graph Convolutional Network for Efficient Body-Part Skeleton Action Recognition

inferred← citeshttps://doi.org/10.1007/978-981-95-0009-3_5openalex-snapshot

Topology-Aware Discriminative Graph Convolutional Network for Skeleton-Based Action Recognition

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Activity Modeling and Recognition

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Dual-View Spatio-Temporal Interactive Network for Video Human Action Recognition

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PhysioFormer: A Spatio-Temporal Transformer for Physical Rehabilitation Assessment

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Multi-level Feature-Based Semantic Guided Neural Network for Skeleton Action Recognition

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Motion-Aware Topology Learning for Skeleton-Based Action Recognition

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A Two-Stream Hybrid CNN-Transformer Network for Skeleton-Based Human Interaction Recognition

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DIDA: Dynamic Individual-to-integrateD Augmentation for Self-supervised Skeleton-Based Action Recognition

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Real-Time Human Action Prediction via Pose Kinematics

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Cross-View Nearest Neighbor Contrastive Learning of Human Skeleton Representation

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Applying 5PKC-Based Skeleton Partition Strategy into Spatio-Temporal Graph Convolution Networks for Fitness Action Recognition

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Skeleton-Based Action Recognition with Combined Part-Wise Topology Graph Convolutional Networks

inferred← citeshttps://doi.org/10.1007/978-981-99-8543-2_34openalex-snapshot

QESAR: Query Effective Decision-Based Attack on Skeletal Action Recognition

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ICE-GCN: An interactional channel excitation-enhanced graph convolutional network for skeleton-based action recognition

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Environmental factors-aware two-stream GCN for skeleton-based behavior recognition

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Action recognition for the robotics and manufacturing automation using 3-D binary micro-block difference

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Skeleton-based action recognition by part-aware graph convolutional networks

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Retrieval of spatial–temporal motion topics from 3D skeleton data

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STA-GCN: two-stream graph convolutional network with spatial–temporal attention for hand gesture recognition

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$$\hbox {PISEP}{^2}$$: pseudo-image sequence evolution-based 3D pose prediction

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Spatio-temporal analysis and comparison of 3D videos

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Effective skeleton topology and semantics-guided adaptive graph convolution network for action recognition

inferred← citeshttps://doi.org/10.1007/s00371-022-02603-1openalex-snapshot

Graph-aware transformer for skeleton-based action recognition

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Interactive semantics neural networks for skeleton-based human interaction recognition

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RETRACTED ARTICLE: Automatically human action recognition (HAR) with view variation from skeleton means of adaptive transformer network

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Unsupervised skeleton-based action representation learning via relation consistency pursuit

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3 s-STNet: three-stream spatial–temporal network with appearance and skeleton information learning for action recognition

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Multi-stream ternary enhanced graph convolutional network for skeleton-based action recognition

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Self-supervised action representation learning from partial consistency skeleton sequences

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Deep learning-based multi-modal approach using RGB and skeleton sequences for human activity recognition

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Efficient combination of classifiers for 3D action recognition

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Attentional weighting strategy-based dynamic GCN for skeleton-based action recognition

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PointDMIG: a dynamic motion-informed graph neural network for 3D action recognition

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Dynamic spatial-temporal topology graph network for skeleton-based action recognition

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A three-stream fusion network for 3D skeleton-based action recognition

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Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognition

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Spatial–Temporal gated graph attention network for skeleton-based action recognition

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Action recognition based on multimode fusion for VR online platform

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Video sketch: A middle-level representation for action recognition

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Action recognition using interrelationships of 3D joints and frames based on angle sine relation and distance features using interrelationships

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Triplet attention multiple spacetime-semantic graph convolutional network for skeleton-based action recognition

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Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition

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Mixed graph convolution and residual transformation network for skeleton-based action recognition

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Focus on temporal graph convolutional networks with unified attention for skeleton-based action recognition

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Spatio-temporal stacking model for skeleton-based action recognition

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Toward action recognition and assessment using SFAGCN and combinative regression model of spatiotemporal features

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An improved spatial temporal graph convolutional network for robust skeleton-based action recognition

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Spatial adaptive graph convolutional network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s10489-023-05173-4openalex-snapshot

Asymmetric information-regularized learning for skeleton-based action recognition

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Clustering-based multi-featured self-supervised learning for human activities and video retrieval

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Motion guided Channel wise Hypergraph convolutional network for skeleton action recognition

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An individualized system of skeletal data-based CNN classifiers for action recognition in manufacturing assembly

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Dual-domain graph convolutional networks for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11042-018-5642-0openalex-snapshot

3D skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN

inferred← citeshttps://doi.org/10.1007/s11042-019-08261-1openalex-snapshot

ConvNets-based action recognition from skeleton motion maps

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An attentional spatial temporal graph convolutional network with co-occurrence feature learning for action recognition

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Two-stream adaptive-attentional subgraph convolution networks for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11042-021-11136-zopenalex-snapshot

Skeleton-based action recognition with temporal action graph and temporal adaptive graph convolution structure

inferred← citeshttps://doi.org/10.1007/s11042-021-11374-1openalex-snapshot

Single and two-person(s) pose estimation based on R-WAA

inferred← citeshttps://doi.org/10.1007/s11042-022-12091-zopenalex-snapshot

3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

inferred← citeshttps://doi.org/10.1007/s11042-022-13298-wopenalex-snapshot

A lightweight graph convolutional network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11042-022-14075-5openalex-snapshot

Real-time human action recognition using raw depth video-based recurrent neural networks

inferred← citeshttps://doi.org/10.1007/s11042-022-14193-0openalex-snapshot

Multimodal human action recognition based on spatio-temporal action representation recognition model

inferred← citeshttps://doi.org/10.1007/s11042-022-14200-4openalex-snapshot

Patient’s actions recognition in hospital’s recovery department based on RGB-D dataset

inferred← citeshttps://doi.org/10.1007/s11042-022-14214-yopenalex-snapshot

Deep learning-based multi-view 3D-human action recognition using skeleton and depth data

inferred← citeshttps://doi.org/10.1007/s11042-023-15334-9openalex-snapshot

Skeleton-based human action recognition by fusing attention based three-stream convolutional neural network and SVM

inferred← citeshttps://doi.org/10.1007/s11042-023-15778-zopenalex-snapshot

Skeleton-based multi-stream adaptive-attentional sub-graph convolution network for action recognition

inferred← citeshttps://doi.org/10.1007/s11042-023-16001-9openalex-snapshot

Human skeleton pose and spatio-temporal feature-based activity recognition using ST-GCN

inferred← citeshttps://doi.org/10.1007/s11042-023-17815-3openalex-snapshot

Deep video representation learning: a survey

inferred← citeshttps://doi.org/10.1007/s11042-024-20562-8openalex-snapshot

CCGS-based discriminatory recognition of skeleton-based actions

inferred← citeshttps://doi.org/10.1007/s11063-021-10585-9openalex-snapshot

Applied Human Action Recognition Network Based on SNSP Features

inferred← citeshttps://doi.org/10.1007/s11063-025-11790-6openalex-snapshot

Dynamic-static partitioning mask and multi-dimensional attention mechanism for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11227-024-06573-0openalex-snapshot

Reconstruction-driven contrastive learning for unsupervised skeleton-based human action recognition

inferred← citeshttps://doi.org/10.1007/s11227-026-08755-4openalex-snapshot

HPCLR: hierarchical part-aware contrastive learning for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11263-020-01354-7openalex-snapshot

View Transfer on Human Skeleton Pose: Automatically Disentangle the View-Variant and View-Invariant Information for Pose Representation Learning

inferred← citeshttps://doi.org/10.1007/s11263-020-01376-1openalex-snapshot

Beyond Covariance: SICE and Kernel Based Visual Feature Representation

inferred← citeshttps://doi.org/10.1007/s11263-021-01467-7openalex-snapshot

Synthetic Humans for Action Recognition from Unseen Viewpoints

inferred← citeshttps://doi.org/10.1007/s11263-022-01594-9openalex-snapshot

Human Action Recognition and Prediction: A Survey

inferred← citeshttps://doi.org/10.1007/s11263-025-02415-5openalex-snapshot

$$\hbox {I}^2$$MD: 3D Action Representation Learning with Inter- and Intra-Modal Mutual Distillation

inferred← citeshttps://doi.org/10.1007/s11265-023-01892-6openalex-snapshot

Structure and Sequencing Preserving Representations for Skeleton-based Action Recognition Relying on Attention Mechanisms

inferred← citeshttps://doi.org/10.1007/s11390-020-0405-6openalex-snapshot

Two-Stream Temporal Convolutional Networks for Skeleton-Based Human Action Recognition

inferred← citeshttps://doi.org/10.1007/s11390-023-2638-7openalex-snapshot

Adversarial Graph Convolutional Network for Skeleton-Based Early Action Prediction

inferred← citeshttps://doi.org/10.1007/s11554-023-01277-9openalex-snapshot

Lightweight multimodal feature graph convolutional network for dangerous driving behavior detection

inferred← citeshttps://doi.org/10.1007/s11554-025-01625-xopenalex-snapshot

A real-time and general method for converting offline skeleton-based action recognition to online ones

inferred← citeshttps://doi.org/10.1007/s11760-020-01781-6openalex-snapshot

View transform graph attention recurrent networks for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11760-022-02465-zopenalex-snapshot

Combining channel-wise joint attention and temporal attention in graph convolutional networks for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11760-023-02829-zopenalex-snapshot

Computer vision-based approach for skeleton-based action recognition, SAHC

inferred← citeshttps://doi.org/10.1007/s11760-025-04246-wopenalex-snapshot

FTIReg: Frequency-time integration with invariant features for skeleton-based human action recognition

inferred← citeshttps://doi.org/10.1007/s11760-025-04356-5openalex-snapshot

LSTF-GCN: local spatio-temporal feature fusion graph convolutional network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s11760-025-04966-zopenalex-snapshot

SAD-GCN: Semantic-aware deformable graph convolutional networks for human abnormal behavior recognition

inferred← citeshttps://doi.org/10.1007/s13042-021-01441-2openalex-snapshot

Hybrid two-stream dynamic CNN for view adaptive human action recognition using ensemble learning

inferred← citeshttps://doi.org/10.1007/s13042-026-03000-zopenalex-snapshot

Deep learning for 3D skeleton-based action recognition: a comprehensive review of methods, datasets, and future directions

inferred← citeshttps://doi.org/10.1007/s13042-026-03173-7openalex-snapshot

A multimodal human action recognition algorithm integrating semantic similarity and spatiotemporal feature fusion

inferred← citeshttps://doi.org/10.1007/s13735-023-00301-9openalex-snapshot

ConvST-LSTM-Net: convolutional spatiotemporal LSTM networks for skeleton-based human action recognition

inferred← citeshttps://doi.org/10.1007/s13735-024-00341-9openalex-snapshot

3D skeleton-based human motion prediction using spatial–temporal graph convolutional network

inferred← citeshttps://doi.org/10.1007/s40747-022-00914-3openalex-snapshot

Two-stream temporal enhanced Fisher vector encoding for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1007/s40747-024-01558-1openalex-snapshot

Attention-guided mask learning for self-supervised 3D action recognition

inferred← citeshttps://doi.org/10.1007/s41019-020-00123-3openalex-snapshot

Deep-Aligned Convolutional Neural Network for Skeleton-Based Action Recognition and Segmentation

inferred← citeshttps://doi.org/10.1007/s41095-021-0218-8openalex-snapshot

Inferring object properties from human interaction and transferring them to new motions

inferred← citeshttps://doi.org/10.1007/s44196-025-00802-xopenalex-snapshot

Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

inferred← citeshttps://doi.org/10.1016/j.adhoc.2020.102384openalex-snapshot

Disentangling style on dynamic aligned poses for individual identification

inferred← citeshttps://doi.org/10.1016/j.asoc.2023.110536openalex-snapshot

Multi-stream Global–Local Motion Fusion Network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.asoc.2023.110575openalex-snapshot

Fast 3D-graph convolutional networks for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.birob.2022.100062openalex-snapshot

Human action recognition using a convolutional neural network based on skeleton heatmaps from two-stage pose estimation

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111595openalex-snapshot

Projection graph convolution and variable-granularity feature shuffling with spatiotemporally separated feature enhancement: A lightweight 3D GCN for human action recognition

inferred← citeshttps://doi.org/10.1016/j.cag.2022.12.008openalex-snapshot

Multi-channel network: Constructing efficient GCN baselines for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.chb.2024.108482openalex-snapshot

Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2022.108112openalex-snapshot

Adaptive 2D skeleton deformation based on view agnostic network for action recognition

inferred← citeshttps://doi.org/10.1016/j.cviu.2018.07.003openalex-snapshot

CuDi3D: Curvilinear displacement based approach for online 3D action detection

inferred← citeshttps://doi.org/10.1016/j.cviu.2021.103348openalex-snapshot

Enhanced discriminative graph convolutional network with adaptive temporal modelling for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.cviu.2022.103491openalex-snapshot

An asymmetrical-structure auto-encoder for unsupervised representation learning of skeleton sequences

inferred← citeshttps://doi.org/10.1016/j.cviu.2023.103655openalex-snapshot

Global–local contrastive multiview representation learning for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.103974openalex-snapshot

SpATr: MoCap 3D human action recognition based on spiral auto-encoder and transformer network

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.104012openalex-snapshot

Static graph convolution with learned temporal and channel-wise graph topology generation for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.104076openalex-snapshot

EnsCLR: Unsupervised skeleton-based action recognition via ensemble contrastive learning of representation

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.104213openalex-snapshot

A GCN and Transformer complementary network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.104229openalex-snapshot

Multi-Scale Adaptive Skeleton Transformer for action recognition

inferred← citeshttps://doi.org/10.1016/j.engappai.2022.104675openalex-snapshot

Temporal segment graph convolutional networks for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106855openalex-snapshot

Feature reconstruction graph convolutional network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.engappai.2026.114253openalex-snapshot

Spatiotemporal interactive multiple self-attention network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.eswa.2021.116424openalex-snapshot

3D Human Action Recognition: Through the eyes of researchers

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120683openalex-snapshot

Skeleton-based action recognition with local dynamic spatial–temporal aggregation

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122538openalex-snapshot

Transfer learning and its extensive appositeness in human activity recognition: A survey

inferred← citeshttps://doi.org/10.1016/j.gmod.2025.101306openalex-snapshot

FD-GCN: Feedback Directed Graph Convolutional Network for skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.image.2019.115659openalex-snapshot

Adaptive lossless compression of skeleton sequences

inferred← citeshttps://doi.org/10.1016/j.image.2023.116940openalex-snapshot

Multi-level channel attention excitation network for human action recognition in videos

inferred← citeshttps://doi.org/10.1016/j.image.2025.117329openalex-snapshot

Hierarchical contrastive learning for unsupervised 3D action recognition

inferred← citeshttps://doi.org/10.1016/j.imavis.2023.104689openalex-snapshot

Cross-stream contrastive learning for self-supervised skeleton-based action recognition

inferred← citeshttps://doi.org/10.1016/j.imavis.2023.104750openalex-snapshot

ATOM: Self-supervised human action recognition using atomic motion representation learning

inferred← citeshttps://doi.org/10.1016/j.imavis.2026.106192openalex-snapshot

HO-GCN: Human–Object Adaptive Topological Interaction GCN for escalator dangerous behavior recognition

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